Compressive cyclostationary spectrum sensing with a constant false alarm rate

Andreas Bollig*, Anastasia Lavrenko, Martijn Arts, Rudolf Mathar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. To that end, a number of different spectrum sensing method have been developed in the literature. Cyclostationary detection is a particular sensing approach that takes use of the built-in periodicities characteristic to most man-made signals. It offers a compromise between achievable performance and the amount of prior information needed. However, it often requires a significant amount of data in order to provide a reliable estimate of the cyclic autocorrelation (CA) function. In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing. Particularly, we propose two compressive spectrum sensing algorithms that exploit further prior information on the CA structure. In the first one, we make use of the joint sparsity of the CA vectors with regard to the time delay, while in the second one, we introduce structure dictionary to enhance the reconstruction performance. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature.

Original languageEnglish
Article number135
Number of pages13
JournalEURASIP journal on wireless communications and networking
Volume2017
DOIs
Publication statusPublished - 1 Dec 2017
Externally publishedYes

Keywords

  • Cyclostationarity
  • Sparse recovery
  • Spectrum sensing

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